Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: An apparatus and method for making fractional assignments of processing elements to processing nodes for stream-based applications in a distributed computer system includes determining an amount of processing power to give to each processing element. Based on a list of acceptable processing nodes, a determination of fractions of which processing nodes will work on each processing element is made. To update allocations of the amount of processing power and the fractions, the process is repeated.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: An apparatus and method for making fractional assignments of processing elements to processing nodes for stream-based applications in a distributed computer system includes determining an amount of processing power to give to each processing element. Based on a list of acceptable processing nodes, a determination of fractions of which processing nodes will work on each processing element is made. To update allocations of the amount of processing power and the fractions, the process is repeated.

Abstract: An apparatus and method for scheduling stream-based applications in a distributed computer system includes a scheduler configured to schedule work using three temporal levels. Each temporal level includes a method. A macro method is configured to schedule jobs that will run, in a highest temporal level, in accordance with a plurality of operation constraints to optimize importance of work. A micro method is configured to fractionally allocate, at a medium temporal level, processing elements to processing nodes in the system to react to changing importance of the work. A nano method is configured to revise, at a lowest temporal level, fractional allocations on a continual basis.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: An apparatus and method for making fractional assignments of processing elements to processing nodes for stream-based applications in a distributed computer system includes determining an amount of processing power to give to each processing element. Based on a list of acceptable processing nodes, a determination of fractions of which processing nodes will work on each processing element is made. To update allocations of the amount of processing power and the fractions, the process is repeated.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.

Abstract: Broadly, techniques for solving network routing within a predetermined error are disclosed. These techniques may be applied to networks supporting dedicated reserve capacity, where reserved capacity on links in the network is dedicated for a particular commodity (generally, a source and sink pair of computers), and shared recovery, where reserved capacity on links is shared amongst two or more commodities. These techniques use an iterative process to determine flows on each of the links in a network. Costs are set for each commodity, and primary and secondary (i.e., backup) flows are initialized. A commodity is selected and demand for the commodity is routed through the shortest path. Costs are updated for each potential failure mode. For each commodity, the flows and costs are updated. Once all flows and costs are updated, then it is determined if a function is less than a predetermined value.

Abstract: Broadly, techniques for solving network routing within a predetermined error are disclosed. These techniques may be applied to networks supporting dedicated reserve capacity, where reserved capacity on links in the network is dedicated for a particular commodity (generally, a source and sink pair of computers), and shared recovery, where reserved capacity on links is shared amongst two or more commodities. These techniques use an iterative process to determine flows on each of the links in a network. Costs are set for each commodity, and primary and secondary (i.e., backup) flows are initialized. A commodity is selected and demand for the commodity is routed through the shortest path. Costs are updated for each potential failure mode. For each commodity, the flows and costs are updated. Once all flows and costs are updated, then it is determined if a function is less than a predetermined value.